I actually majored in both computer science and philosophy at a school with a top 5 CS program and a top 50 philosophy program. I found the combination useful, but eventually grew frustrated with much of the philosophy. My classes often spent more time splitting hairs than trying to say useful things. The philosophy that actually proved most useful were my logic courses, and one or two papers from philosophy of mind.
That said, philosophy is incredibly important. But, as PG has noted, we tend to do a poor job of it (http://paulgraham.com/philosophy.html). We need to spend more time focused on saying useful, testable things. In short, the best scientific results merge with philosophy. So, everyone should be a philosopher, but should do the majority of their philosophy as science.
Again, both philosophy and computer science are important, but after studying both pretty intensely for 3.5 years (I graduated with over 180 credit hours), you have to pick and choose the philosophy. It's mostly useful for setting the initial biases on which the rest of your science will depend and for continuing to think about things science can't speak to, yet.
A related paper just came out in Science a few days ago. The authors used the inner workings of a fruit fly's brain to provide a robust solution to the Maximal Independent Set (MIS) problem.
The instruction set of a properly designed computer must be isomorphic to a minimal, elegant high-level programming language.
I've been a bit confused about the details of the loper-os project. For some reason, this sentence sparked a micro-epiphany and the whole thing came into focus. So, I thought the article, and the project at large, was worth sharing with HN.
Cal Newport also does a great job in keeping up with a lot of the published literature in these fields. Check out his books and blog at http://calnewport.com. It's been incredibly helpful to me over the past few years.
The applications for this sort of material are pretty incredible. Rubbery materials can often tear/degrade easily; I can't tell you how many times I've torn some soft plastic and thought to myself, "Well, that's useless now..." This material has the potential to totally change that.
Also, did anyone think of Superman when reading this story? I couldn't help but be reminded that he, too, is healed by "the power of the yellow sun".
I imagine the research about non-verbal communication in humans would be instructive here. A relatively small minority of our in-person communication actually comes from the spoken words. The rest comes from body language, tone, inflection, and so on.
I wonder how much of that a dog or similar animal picks up on regularly? Could it be that they actually pick up on the majority of our communicated meaning?
What's more, I wonder if we are we actually poorer communicators than animals, because they understand our messages better than we understand theirs?
I'm always a bit awed when recognizable functions like 'if' and 'let' start emerging from a mess of lambdas and Xs. It amazing how simple items and simple rules can combine to create fantastically intricate systems (i.e. the entire space of computable functions). Thanks for sharing, Matt.
The same article, posted on the author's homepage, hit HN about a month ago. There are some pretty good comments there as well. Check it out. http://news.ycombinator.com/item?id=1999010
I will say that, as I'm preparing for grad school, this sort of writing is incredibly helpful. If anyone else is considering writing a piece like this, I encourage you to do so.
I agree that a clearer definition of wrong here. My guess is that it has to do with the solution being empirical and not-guaranteed, rather than analytic and airtight.
Just because the solution isn't perfectly analytic doesn't mean it's wrong, though. In fact, it seems hard to argue that the solutions which work well in natural systems are wrong.
Really, we need a lot more research like this that takes well-working natural systems and distills the underlying model. It seems a bit arrogant to try and independently solve problems when existing solutions are sitting all around us.
So, are you saying, for example, we should get subjects with good copies in these experiments to spend the same amount of time reading as those with bad copies?
The main point of this piece is not that you take time at the end of the day, or in the middle, or a few times throughout. The point is that, whenever you do it, you need to take time to think, time to reflect on what you've been doing. If you never take time to reflect and answer some of the nagging bigger questions, you severely short-change your long-term effectiveness.
I've been running an experiment recently to take at least 15 minutes each day to think about my research (I do neuroscience research for a university in DC). The results have been great, and I'm starting to add in more reflective time throughout the day, even when I'm not 'working'. If you're interested, I have been, and will continue to be, writing about it here: http://joshrule.com/blog. Look for posts about trial 2.